Summary

Using the Logistic Regression Procedure, you have constructed a model for predicting the probability a given customer will default on their loan.

A critical issue for loan officers is the cost of Type I and Type II errors. That is, what is the cost of classifying a defaulter as a non-defaulter (Type I)? What is the cost of classifying a non-defaulter as a defaulter (Type II)? If bad debt is the primary concern, then you want to lower your Type I error and maximize your sensitivity. If growing your customer base is the priority, then you want to lower your Type II error and maximize your specificity. Usually both are major concerns, so you have to choose a decision rule for classifying customers that gives the best mix of sensitivity and specificity.

Next